Intelligent decision supporting system and method for making intelligent decision

ABSTRACT

An intelligent decision supporting system and a method for making an intelligent decision are provided. The intelligent decision supporting system includes a multi-dimensional classifier comprising a plurality of classifiers that define different semantic standards and are trained based on the different semantic standards, for classifying a text by the semantic standards and for outputting a plurality of attributes of the text and a confidence rate of each of the plurality of attributes, a question submitting module for receiving the output of the multi-dimensional classifier, for forming a question based on the plurality of attributes of the text and the confidence rate of each attribute, and for submitting the question to an inference machine, the inference machine for receiving the question submitted by the question submitting module, for inquiring of a domain ontology knowledge library based on the question, and for providing an answer for the question to an decision reply module, a domain ontology knowledge library module for storing a domain ontology knowledge library related to an application domain of the intelligent decision supporting system, wherein the domain ontology knowledge library records description of rules for deriving decisions corresponding to the semantic standards of the multi-dimensional classifier, and the decision reply module for providing the answer for the question provided by the inference machine to the user.

PRIORITY

This application claims the benefit under 35 U.S.C. §119(a) of a Chinesepatent application filed in the State Intellectual Property Office ofthe Peoples Republic of China on Feb. 2, 2010 and assigned Serial No.201010105287.6, the entire disclosure of which is hereby incorporated byreference.

JOINT RESEARCH AGREEMENT

The presently claimed invention was made by or on behalf of the belowlisted parties to a joint research agreement. The joint researchagreement was in effect on or before the date the claimed invention wasmade and the claimed invention was made as a result of activitiesundertaken within the scope of the joint research agreement. The partiesto the joint research agreement are Samsung Electronics Co., LTD., andSamsung Electronics (China) R&D Center.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a technical field of intelligentdecision. More particularly, the present invention relates to anintelligent decision supporting system and a method for makingintelligent decision.

2. Description of the Related Art

With the development of technologies that provide information, such asthe internet, the processing speed and volume of various types ofinformation that are received and processed by people are rapidlyincreasing. When receiving information from different channels, forexample, digital document information such as a webpage on the internet,an email, a digital library etc., people need to make judgments anddecisions according to the information provided by these digitaldocuments. It is an important subject in the field of digital documentprocessing to classify text so as to efficiently and quickly process thedigital document. Text classification refers to the construction of amodel for classification based on the available data, i.e. a classifier.A classifier determines a category for each document in a set of testdocuments according to a predefined classification system, such that auser is able to conveniently browse a document, or to facilitatesearching for documents by limiting the scope of searching Automatictext classification refers to training of a classification rule ormodeling parameters by using a large amount of text with class tags, andrecognize text of an unknown category by using the result of thetraining. Support Vector Machine (SVM) is a well-known method for textclassification, and is widely used. SVM is a pattern recognition methodbased on statistics and learning theory, which shows special advantagesin resolving problems of pattern recognition of small sample, non-linearand high dimensions, and can be applied to other machine learningproblems such as function fitting. SVM is now successfully applied tomany fields such as Bioinformatics, text and handwriting recognitionetc.

A current text classifier is only used to classify text or insert alabel to text for classification. More particularly, the text classifierfirst collects data according to predefined classification levels toform a large quantity of training samples. Then, the text classifierperforms feature extraction and model training on the training samplesto generate a model of text category. Next, the text classifier mayclassify text to be predicted by using the model obtained by training Inparticular, the text classifier pre-processes the text to be predicted,extracts features of the text, and classifies the text by using thegenerated model. The text classifier outputs a confidence rate for eachcategory, and classifies the text to be predicted into a plurality ofcategories according to the confidence rate, or adds a label to the textto be predicted and classifies it.

However, a problem exists in the related art in that the categories intowhich the text is classified by the text classifier are predefined tags,which cannot be used to make an intelligent decision. That is, it isunable to obtain a decision related to the text through textclassification. Thus, an intelligent decision supporting system, whichmay predict an intention or interest of a client by text classificationand other techniques of related art, and provide a feedback opinion orhint to help the user/client to make a decision is needed.

SUMMARY OF THE INVENTION

An aspect of the present invention is to address at least theabove-mentioned problems and/or disadvantages and to provide at leastthe advantages described below. Accordingly, an aspect of the presentinvention is to provide a method for integrating a text classifier and aknowledge domain ontology to make an intelligent decision. Herein,“knowledge domain ontology” may refer to a knowledge database of aspecific technical domain, including a data structure determined byexperts in the art that can be searched. For example, in a knowledgedomain ontology of geography, knowledge information corresponding toattribute information of “travel” and “Beijing” may be “tourist route ofForbidden City”, “Guide of Great Wall”, “Guide of Summer Palace” etc.

Another aspect of the present invention is to provide an intelligentdecision supporting system that includes semantic description of thetext. Here, a classification category corresponding to the textclassifier of each dimension represents a set of semantic standards. Bydoing this, a confidence rate output from the classifier represents aconfidence degree for a category under each semantic standard (includingeach semantic attribute of each standard). The confidence degree of allattributes is input into a question submitting system as input, semanticfusion may be applied to each semantic attribute of multiple categoriesto output an intelligent decision. This intelligent decision is notlimited to predefined categories. Fusion of semantic deduction will givea more intelligent decision to satisfy the user's requirement. In orderto achieve intelligent semantic fusion, each set of standards of aclassifier of multi-dimensions is required not to overlap with eachother, and corresponds to content of the knowledge domain ontology. Thatis, a category and attribute which are defined by each set of standardsof text classifier of each dimension should be included within the scopeof description of the knowledge domain ontology.

According to an aspect of present invention, an intelligent decisionsupporting system is provided. The system includes a multi-dimensionalclassifier, comprising a plurality of classifiers that define differentsemantic standards and are trained based on the different semanticstandards, for classifying a text by the semantic standards and foroutputting a plurality of attributes of the text and a confidence rateof each of the plurality of attributes of a text, a question submittingmodule for receiving the output of the multi-dimensional classifier, forforming a question based on the plurality of attributes of the text andthe confidence rate of each attribute, and for submitting the questionto an inference machine, the inference machine for receiving thequestion submitted by the question submitting module, for inquiring of adomain ontology knowledge library based on the question, and forproviding an answer for the question to a decision reply module, adomain ontology knowledge library module for storing a domain ontologyknowledge library related to an application domain of the intelligentdecision supporting system, wherein the domain ontology knowledgelibrary records descriptions of rules for deriving decisionscorresponding to the semantic standards of the multi-dimensionalclassifier, and the decision reply module for providing the answer forthe question provided by the inference machine to the user.

According to another aspect of present invention, a method for making anintelligent decision is provided. The method includes, defining semanticstandards of a plurality of classifiers of a multi-dimensionalclassifier according to an application domain and recording descriptionsof rules in a domain ontology knowledge library for deriving decisionsthat correspond to the semantic standards of the multi-dimensionalclassifier, collecting training texts according to the semanticstandards, and training the multi-dimensional classifier, classifying atext to be analyzed by using multiple standards, and outputtingattributes of the text and confidence rate of each attribute, forming aquestion for intelligent decision based on attributes of the text andthe confidence rate of each attribute, and inquiring of the domainontology knowledge library based on the question by an inference machineto obtain an answer for intelligent decision and providing the answer toa user.

Other aspects, advantages, and salient features of the invention willbecome apparent to those skilled in the art from the following detaileddescription, which, taken in conjunction with the annexed drawings,discloses exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainexemplary embodiments of the present invention will be apparent from thefollowing description taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a diagram illustrating a configuration of an intelligentdecision supporting system according to an exemplary embodiment of thepresent invention;

FIG. 2 illustrates an advertisement recommending system including anintelligent decision supporting system according to an exemplaryembodiment of present invention; and

FIG. 3 is a diagram illustrating a method for applying an intelligentdecision supporting system according to an exemplary embodiment ofpresent invention.

Throughout the drawings, it should be noted that like reference numbersare used to depict the same or similar elements, features, andstructures.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The following description with reference to the accompanying drawings isprovided to assist in a comprehensive understanding of exemplaryembodiments of the invention as defined by the claims and theirequivalents. It includes various specific details to assist in thatunderstanding but these are to be regarded as merely exemplary.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the embodiments described hereincan be made without departing from the scope and spirit of theinvention. In addition, descriptions of well-known functions andconstructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are notlimited to the bibliographical meanings, but, are merely used by theinventor to enable a clear and consistent understanding of theinvention. Accordingly, it should be apparent to those skilled in theart that the following description of exemplary embodiments of thepresent invention is provided for illustration purpose only and not forthe purpose of limiting the invention as defined by the appended claimsand their equivalents.

It is to be understood that the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.Thus, for example, reference to “a component surface” includes referenceto one or more of such surfaces.

FIG. 1 is a diagram illustrating a configuration of an intelligentdecision supporting system according to an exemplary embodiment of thepresent invention.

Referring to FIG. 1, the intelligent decision supporting system includesa multi-dimensional classifier 100, a question submitting module 200, aninference engine 300, a domain ontology knowledge library 400, aknowledge library management module 500 and a decision replying module600.

The multi-dimensional classifier 100 may include a plurality of standardclassifiers. The multi-dimensional classifier includes n standardclassifiers, i.e. a first standard classifier to an nth standardclassifier. Herein, the term “standard” refers to a set of semanticstandards including multiple attributes. In an exemplary implementation,contents covered by each semantic standard do not overlap with eachother, but are complementary with those of other semantic standards inan aspect of semantic aspect. In other words, a semantic standard refersto a standard for comprehension of the same text in different views. Thestandard may be considered as an abstract concept of a class andexamples of the abstract concept, which represent a standard forcomprehending a text from a certain point of view by human beings. Eachsemantic attribute of each set of semantic standards may be a semantictag for describing the text. By using the multi-dimensional classifier100, attribute description of the text to be analyzed for providingdecisions may be obtained in different semantic domains. Theclassification standard of the multi-dimensional classifier 100 may bedifferent depending on the application domain, and the object and mannerto which the intelligent decision supporting system is applied. Examplesof multi-dimensional standards will be described in more detail below.

After the semantic standard of the multi-dimensional classifier 100 isdefined, a large quantity of text is needed to be collected for trainingeach standard classifier. The multi-dimensional classifier may betrained in the following way. First, features of sample text to betrained are extracted to obtain an eigenvector space of the text. Thismay be implemented by a variety of methods for feature extraction. Forexample, a general Term Frequency-Inverse Document Frequency (TF-IDF)method may be used, or a more complicated method based on WordNet orother algorithm may be used. Methods for feature extraction intend togenerate an eigenspace for describing the text in each semanticstandard. Each sample text is described by eigenvectors based on aneigenspace. Eigenvectors of all texts in each semantic standard of themulti-dimensional classifier 100 are trained to obtain a training modelof each standard classifier. The training model of each standardclassifier may be used to predict a respective confidence rate (i.e.,confidence degree) of attributes in a semantic standard. The confidencerate is generally expressed by a probability value between 0 to 1.

After the training model of a standard classifier is obtained, when atext to be analyzed is input into the multi-dimensional classifier 100,the text is classified in multiple standards by each standard classifierof the multi-dimensional classifier 100, using the training modelthereof, such that a description of multiple semantic attributes of thetext is obtained, as well as a value of confidence degree for eachsemantic attribute. Herein, the multi-dimensional classifier 100 outputsthe classified multiple semantic attributes and the value of confidencedegree of each semantic attribute to the question submitting module 200.

The question submitting module 200 pre-processes the multiple semanticattributes based on the received confidence rates of the multiplesemantic attributes, and forms a question required by the knowledgelibrary management module 500. The form of question is differentdepending on the applied domain. An example of forming the question willbe described in more detail below. After the question is obtained, thequestion submitting module 200 initiates the inference machine 300, andsubmits the formed question to the inference machine 300. Here, thequestion may be submitted in various forms. For example, the questionmay be submitted as eXtensible Mark Language (XML).

The inference machine 300 analyzes the attribute of the question andinquires of the knowledge library stored in the domain ontologyknowledge library module 400 according to the attribute of the question,after receiving the question from the question submitting module 200.The knowledge library records rules for deriving an answer for aquestion according to the attribute of the question. These rules may bespecified or defined by experts in the art. According to an exemplaryembodiment of present invention, the semantic standard (i.e., theattribute of the question) used by the multi-dimensional classifier 100has a corresponding relationship with the deriving rules of the domainontology knowledge library. That is, each semantic standard has itscorresponding description in the domain ontology knowledge library.However, the semantic scope defined by the domain ontology knowledgelibrary is much larger than the semantic standard defined by themulti-dimensional classifier. For example, the rules of the domainontology knowledge library may correspond to a combination of at leastone attribute. This will be described in more detail with reference toFIGS. 2 and 3.

The domain ontology knowledge library module 400 returns a result of theinquiry to the inference machine 300. The inference machine 300 forms anintelligent decision in a form defined by the knowledge librarymanagement module 500, based on the inquired result, and transfers thedecision to the decision reply module 600. Here, the knowledge librarymanagement module 500 is used to configure the knowledge library storedin the domain ontology knowledge library module 400 and the form inwhich the question is submitted by the question submitting module 200.That is, the form of the question generated by the question submittingmodule 200 may be varied by the knowledge library management module 500,or the rules for deriving answers in the knowledge library can beupdated by the knowledge library management module 500. The functions ofthe knowledge library management module 500 may be integrated intofunctions of the question submitting module 200 and the domain ontologyknowledge library module 400, or be omitted.

The decision reply module 600 converts the decision into a form that maybe recognized by the user, and finally outputs an answer of the decisionto a user. Here, the form of outputting the decision may be differentdepending on the applied domain. This will be described in more detailwith reference to FIGS. 2 and 3.

It will be understood that the functions of the modules may beintegrated into a single module, or divided into more sub-modules.

FIG. 2 illustrates an automatic advertisement recommending systemincluding an intelligent decision supporting system according to anexemplary embodiment of the present invention.

Referring to FIG. 2, the automatic advertisement recommending systemalso comprises a multi-dimensional classifier, a question submittingmodule, an inference machine, an ontology knowledge library of advertisedomain, a knowledge library management module, and a decision replymodule.

More particularly, in this automatic advertisement recommending system,the multiple dimensional standards of the multi-dimensional classifierare defined as three categories: country, advertisement and sensitive.That is, the multi-dimensional classifier comprises 3 standardclassifiers. Each standard comprises a plurality of attributes. Forexample, the standard “country” includes attributes such as China, U.S.,Japan, Germany, etc. The standard “advertisement” includes categories ofvarious products, such as automobile, drink, house appliance, etc. Thestandard “sensitive” includes sensitivity categories of theadvertisement, such as accident, obscene, etc. The categories ofattributes shown here are only illustrative and they are not intended tolimit the present invention. In the advertisement recommending systemaccording to an exemplary embodiment of present invention, themulti-dimensional classifier based on SVM is different from conventionalkeyword classification technique in that the attribute of a classifierof each dimension further covers extended attributes related to theattribute. Generally, a simple keyword classification techniquerecognizes an attribute represented by a keyword only when the keywordis present in the text. The multi-dimensional classifier of an exemplaryembodiment of the present invention does not depend solely on theattribute of keyword when recognizing attribute of the text. Using theattribute “China” of the classifier of geography domain as an example,not only a displayed geographic word “Beijing” is considered as anattribute, but also words and phases having semantics of Chineseelements are considered as extended attributes, such as dumplings,Forbidden City, golden week, red tourism, etc. In the case that wordsrelated to the keyword, such as dumpling, Forbidden City, exist in thetext, the category of attribute “country” of the text is recognized as“China”. That is, when a keyword of the extended attribute which isassociated with an attribute category exists in the text, themulti-dimensional classifier of the present invention is able torecognize the attribute of the text.

A large quantity of text samples are collected for each standardclassifier, and eigenvectors of the text samples are extracted. Here,the multi-dimensional classifier removes words that do not carrysemantic information, and restores each word into its prototype. Forexample, if a word is a past tense verb, this verb is restored intoprototype. Then, frequencies of a word and a document are calculated byusing a TF-IDF method to obtain their weights. All of the words are usedas an eigenspace of a classifier to obtain eigenvectors of each text. Acategory model may be obtained by training of the eigenvectors. Afterobtaining the category model, a prediction may be performed on a text ofunknown category to obtain a confidence rate of each attribute of thetext. As shown in FIG. 2, in the output of the classifier of category“geography”, the confidence rate of “China” is 0.8, and the confidencerate of “U.S.” is 0.4; in the output of the classifier of category“advertisement”, the confidence rate of “automobile” is 0.8, and theconfidence rate of “drink” is 0.8; and in the output of the classifierof category “sensitive”, the confidence rate of “accident” is 0.9, andthe confidence rate of “obscene” is 0.1. The confidence rates of all theattributes are used as input of the question submitting module, and thequestion submitting module forms a question. If the threshold of theconfidence rate is set to 0.8, attributes that have confidence ratesequal to or higher than 0.8 are selected to form the question to besubmitted. In this advertisement recommending system, the confidencerates of “automobile” and “drink” are relatively higher. In addition,considering that the confidence rate of “accident” is also high,“automobile” is excluded from being an attribute for forming a question.That is, the question submitting module generates a question based onattributes “China” and “drink”, and submits the generated question tothe inference machine

The inference machine makes an inquiry of the ontology knowledge libraryof the advertisement domain according to the question, obtains aninference result, and transfers the inference result to the decisionreply module to make a conversion for the user. Finally, the user seesthe decision of the system, i.e. which advertisement is recommended. Asshown in FIG. 2, based on advertisements associated with attributes“China” and “drink” which are defined in the ontology knowledge libraryof the advertisement domain, the final decision gives an advertisementrelated to Chinese drink, such as “Wanglaoji”. On the contrary, sincethe attribute “U.S.” is excluded due to a low confidence rate, theadvertisement recommending system will not recommend an advertisementrelated to an American drink. Herein, the decision reply module providesthe user with a decision reply in the form of an advertisement (forexample, providing a link and a picture of the advertisement onwebpage).

In addition, according to an exemplary embodiment of present invention,the advertisement recommending system may further recommend a pluralityof appropriate advertisements according to the output of themulti-dimensional classifier. The output of the inference machine can beset to provide a predetermined number of advertisements required by theuser or designer. For example, the number of recommended advertisementmay be set to more than 1.

FIG. 3 is a diagram illustrating a method for applying an intelligentdecision supporting system according to an exemplary embodiment of thepresent invention.

Referring to FIG. 3, an example is provided of an intelligent decisionsupporting system regarding a medical diagnosis. That is, theintelligent diagnosis supporting system is used to automatically providea diagnosis scheme or provide a prescription based on a diagnosis recordof a doctor. In the illustrated exemplary embodiment, the intelligentdiagnosis supporting system comprises a multi-dimensional classifier, aquestion submitting module, an inference machine, an ontology knowledgelibrary of medical domain, a knowledge library management module and adecision reply module. The multi-dimensional classifier is composed ofmedical records of doctors in different medical departments. Forexample, a first dimensional classifier is a medical record of a medicalinsurance department, the second dimensional classifier is a medicalrecord of a body temperature department, and the third dimensionalclassifier is medical record of a pneumonia department, or the like. Ofcourse, medical experts may define the standard dimension of themulti-dimensional classifier. The multi-dimensional classifier istrained on the basis of a large amount of medical records, such that theclassifier of each dimension represents a comprehensive assessment ofdisease of a patient in one point of view. By doing this, a trainingmodel of the classifier of each dimension is obtained. Then, a medicalrecord to be analyzed is analyzed by using the training model of eachdimension of the multi-dimensional classifier. In FIG. 3, the medicalrecord to be analyzed may be a text which describes the symptoms of apatient. For example, the medical record may describe that “thetemperature of patient is 37.5 degrees centigrade, no infection in lung,the number of medical insurance is xxxx . . . etc.” Themulti-dimensional classifier analyzes the text and predicts thefollowing attributes: “no pneumonia”, “low fever” and “having medicalinsurance”. That is, the three attributes having the highest confidencerates are “no pneumonia”, “low fever” and “having medical insurance”,which are obtained by the training model of the multi-dimensionalclassifier. Then, a question to be submitted to the inference machine isgenerated based on the above three attributes. FIG. 3 illustrates aquestion in XML form. The inference machine searches for an answer forthis question in an ontology knowledge library of a medical domain.There are three rules recorded in the ontology knowledge library. Rule1: “catch cold” is derived from (no pneumonia+low fever). Rule 2: “allmedicine prescribed by medical insurance” is derived from (havingmedical insurance). Rule 3: “medicine Sanjiu Ganmaoling” is derived from(having medical insurance+catch cold). Finally, the inference machineobtains an answer for the question. That is, the final decision (anintelligent prescription) is “medicine Sanjiu Ganmaoling”.

It will be understood that an exemplary intelligent decision supportingsystem of present invention provides an intelligent system for helping auser make a decision by combining a knowledge library of domain ontologywith a multi-dimensional text classifier. An exemplary intelligentdecision supporting system of present invention differs from theconventional text classifier in that, the present invention does notsimply classify the text or provide a tag for the text according to akeyword or other standard. Instead, exemplary embodiments of the presentinvention provide a decision which complies with the mode of the humanmind according to the applied knowledge domain. This improves theefficiency of processing text and decision making based on the processedtext, and provides convenience that the prior text processing methodcannot provide.

While the invention has been shown and described with reference tocertain exemplary embodiments thereof, it will be understood by thoseskilled in the art that various changes in form and details may be madetherein without departing from the spirit and scope of the invention asdefined by the appended claims and their equivalents.

1. An intelligent decision supporting system, the system comprising: amulti-dimensional classifier, comprising a plurality of classifiers thatdefine different semantic standards and are trained based on thedifferent semantic standards, for classifying a text by the semanticstandards and for outputting a plurality of attributes of the text and aconfidence rate of each of the plurality of attributes; a questionsubmitting module for receiving the output of the multi-dimensionalclassifier, for forming a question based on the plurality of attributesof the text and the confidence rate of each attribute, and forsubmitting the question to an inference machine; the inference machinefor receiving the question submitted by the question submitting module,for inquiring of a domain ontology knowledge library based on thequestion, and for providing an answer for the question to a decisionreply module; a domain ontology knowledge library module for storing adomain ontology knowledge library related to an application domain ofthe intelligent decision supporting system, wherein the domain ontologyknowledge library records descriptions of rules for deriving decisionscorresponding to the semantic standards of the multi-dimensionalclassifier; and the decision reply module for providing the answer forthe question provided by the inference machine to a user.
 2. The systemof claim 1, further comprising: a knowledge library management modulefor configuring the rules for deriving the domain ontology knowledgelibrary stored in the domain ontology knowledge library module and theform of the question of the question submitting module.
 3. The system ofclaim 1, wherein the multi-dimensional classifier comprises a SupportingVector Machine (SVM).
 4. The system of claim 1, wherein the rules forderiving decisions in the domain ontology knowledge library correspondto combinations of semantic standards of the multi-dimensionalclassifier.
 5. The system of claim 1, wherein the multi-dimensionalclassifier collects training texts according to semantic standards,extracts eigenvectors of the training texts to form an eigenvectorspace, describes each training text with eigenvectors based on theeigenvector space, and predicts and outputs attributes of the text to bepredicted and confidence rate of each attribute based on the trainingmodel of each classifier.
 6. The system of claim 5, wherein themulti-dimensional classifier uses a Term Frequency-Inverse DocumentFrequency (TF-IDF) method to extract eigenvectors of the training texts.7. The system of claim 1, wherein the confidence rate comprises aprobability value between 0 and
 1. 8. The system of claim 1, wherein thequestion is submitted to the inference machine as eXtensible MarkupLanguage (XML).
 9. A method for making an intelligent decision, themethod comprising: defining semantic standards of a plurality ofclassifiers of a multi-dimensional classifier according to anapplication domain and recording descriptions of rules in a domainontology knowledge library for deriving decisions that correspond to thesemantic standards of the multi-dimensional classifier; collectingtraining texts according to the semantic standards, and training themulti-dimensional classifier; classifying a text to be analyzed by usingmultiple standards, and outputting attributes of the text and confidencerate of each attribute; forming a question for intelligent decisionbased on attributes of the text and the confidence rate of eachattribute; and inquiring of the domain ontology knowledge library basedon the question by an inference machine to obtain an answer forintelligent decision and providing the answer to a user.
 10. The methodof claim 9, wherein the collecting of the training texts according tothe semantic standards comprises: collecting training texts according tosemantic standards, by the multi-dimensional classifier; extractingeigenvectors of the training texts to form an eigenvector space;describing each training text with eigenvectors based on the eigenvectorspace, to form a training model for each classifier; and predicting andoutputting attributes of the text and confidence rate of each attributebased on the training model of each classifier.
 11. The method of claim10, further comprising using a Term Frequency-Inverse Document Frequency(TF-IDF) method to extract eigenvectors of a training text.
 12. Themethod of claim 9, wherein the rules for deriving decisions in thedomain ontology knowledge library correspond to combinations of semanticstandards of the multi-dimensional classifier.
 13. The method of claim9, wherein the confidence rate comprises a probability value between 0and
 1. 14. The method of claim 9, wherein the question is submitted tothe inference machine as eXtensible Markup Language (XML).